Abstract
We consider the learning capabilities of FIN- type learners when they are allowed to change their hypothesis at most once. We consider probabilistic learners of this sort as well as pluralistic learners (i.e., teams of deterministic learners). We show for probabilities in the interval (5/6, 1] that the capabilities of the probabilistic learners are precisely divided into intervals of the form (5n+6/6n+7, 5n+1/6n+1] for all n≥0. We show that teams of such learners with plurality r/s (i.e., a team of s learners such that at least τ always successfully learn) are equivalent to probabilistic learners with probability of success τ/s. We also show that at the ratio 5/6 redundancy pays for team learners (i.e., a team with plurality 10/12 is more powerful than a team with plurality 5/6). Moreover, for any r and s with r/s=5/6 we show that any team of learners with plurality r/s is equivalent to a team with plurality 5/6 if r is odd, and to a team with plurality 10/12 if r is even.
Preview
Unable to display preview. Download preview PDF.
References
R. Daley, and B. Kalyanasundaram, Capabilities of Probabilistic Learners with Bounded Mind Changes, Submitted for Publication.
R. Daley, B. Kalyanasundaram, and M. Velauthapillai, Breaking the probability 1/2 barrier in FIN-type learning, In Proceedings of the 1992 Workshop on Computational Learning Theory, 1992.
R. Daley, B. Kalyanasundaram, and M. Velauthapillai, The Power of Probabilism in Popperian FINite Learning, to appear in AII, 1992.
R. Daley, L. Pitt, M. Velauthapillai, and T. Will, Relations between probabilistic and team one-shot learners, In Proceedings of the 1991 Workshop on Computational Learning Theory, pages 228–239, 1991.
R.V. Freivalds, Finite Identification of General Recursive Functions by Probabilistic Strategies, Akademie Verlag, Berlin, 1979.
E. M. Gold, Language identification in the limit, Information and Control, 10:447–474, 1967.
S. Jain, and A. Sharrna, Finite learning by a team, In Proceedings of the 1990 Workshop on Computational Learning Theory, pages 163–177, 1990.
L. Pitt, Probabilistic inductive inference, J. ACM, 36(2):383–433, 1989.
L. Pitt, and C. Smith, Probability and plurality for aggregations of learning machines, Information and Computation, 77(1):77–92, 1988.
C. H. Smith, The power of pluralism for automatic program synthesis, J. ACM, 29:1144–1165, 1982.
M. Velauthapillai, Inductive inference with a bounded number of mind changes, In Proceedings of the 1989 Workshop on Computational Learning Theory, pages 200–213, 1989.
R. Wiehagen, R. Freivalds, and E. Kinber, On the power of probabilistic strategies in inductive inference, Theoretical Computer Science, 111–113, 1984.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Daley, R., Kalyanasundaram, B. (1992). Probabilistic and pluralistic learners with mind changes. In: Havel, I.M., Koubek, V. (eds) Mathematical Foundations of Computer Science 1992. MFCS 1992. Lecture Notes in Computer Science, vol 629. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-55808-X_20
Download citation
DOI: https://doi.org/10.1007/3-540-55808-X_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55808-8
Online ISBN: 978-3-540-47291-9
eBook Packages: Springer Book Archive